In accordance with the increasing demands for high-speed and high-quality data transmissions, a Multiple-Input Multiple-Output (MIMO) wireless communication system using a plurality of transmit antennas and receive antennas is attracting much attention as a technique to satisfy those demands. The MIMO technology performs communications using a plurality of streams via the antennas to thus greatly enhance the channel capacity compared to a single-antenna system. For example, when the transmitting end and the receiving end employ M-ary transmit antennas and M-ary receive antennas, respectively, channels of the antennas are independent of each other, and a bandwidth and a total transmit power are fixed, an average channel capacity increases by M times the single antenna.
Recently, the adoption of a Closed Loop (CL) MIMO system is under consideration. In the CL MIMO system, the transmitting end acquires channel conditions of the receiving end and determines per stream Modulation and Coding Scheme (MCS) levels based on the channel conditions of the receiving end. For doing so, the receiving end feeds per stream Channel Quality Information (CQI) back to the transmitting end. The receiving end needs to generate the per stream CQI using its channel information.
When the receiving end uses a Minimum Mean Square Error (MMSE) detection scheme or an MMSE-Ordered Successive Interference Cancellation (OSIC) detection scheme, it is easy to generate the per stream CQI (e.g., Signal to Interference and Noise Ratio (SINR)). In contrast, when the receiving end uses a Maximum Likelihood (ML) detection scheme or a lattice-reduction-aided detection scheme, the generation of the per stream CQI is quite complicated because signals of all streams are detected in one unit. Thus, to apply the ML scheme or the lattice-reduction-aided scheme into the CL MIMO system, a method for generating feedback information suitable to the ML scheme or the lattice-reduction-aided scheme is needed.